Machine learning to improve frequent emergency department use prediction: a retrospective cohort study.

ABSTRACT: Frequent emergency department use is associated with many adverse events, such as increased risk for hospitalization and mortality. Frequent users have complex needs and associated factors are commonly evaluated using logistic regression. However, other machine learning models, especially those exploiting the potential of large databases, have been less explored. This study aims at comparing the performance of logistic regression to four machine learning models for predicting frequent emergency department use in an adult population with chronic diseases, in the province of Quebec (Canada). This is a retrospective population-based study using medical and administrative databases from the Régie de l'assurance maladie du Québec. Two definitions were used for frequent emergency department use

SUBMITTER: Chiu YM 

PROVIDER: S-EPMC9898278 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature